Fake News Detection in Social Networks Using Data Mining Techniques

Abstract

Fake news is propagated by intentionally spreading false information on social media platforms. Fake news intends to mislead the public and damage the reputation of a person or entity. Detecting misinformation over digital platforms is essential to minimizing its adverse effects. While false comments and news can be easily posted on social media without any oversight, identifying real information from false information is often the most challenging part. This work examined the most relevant features that can be used for fake news detection. After selecting the significant features, prediction models are built and compared in terms of precision, recall, and F-score evaluation metrics using Naive Bayes, Bayesian Network, and J48 classification methods. Based on our experiments on a benchmark dataset, we obtained an overall F-score of 69.7% by employing the J48 classifier on the politician's brief statement, and the counts of the speaker's statement history feature set.